publications
#pag. 14
Effects of Heterogeneous Social Interactions on Flocking Dynamics
Carmen Miguel, M.; Parley, Jack T; Pastor-Satorras, Romualdo
PHYSICAL REVIEW LETTERS
120
068303
(2018)
Social relationships characterize the interactions that occur within social species and may have an important impact on collective animal motion. Here, we consider a variation of the standard Vicsek model for collective motion in which interactions are mediated by an empirically motivated scale-free topology that represents a heterogeneous pattern of social contacts. We observe that the degree of order of the model is strongly affected by network heterogeneity: more heterogeneous networks show a more resilient ordered state, while less heterogeneity leads to a more fragile ordered state that can be destroyed by sufficient external noise. Our results challenge the previously accepted equivalence between the static Vicsek model and the equilibrium XY model on the network of connections, and point towards a possible equivalence with models exhibiting a different symmetry.
Micro-flock patterns and macro-clusters in chiral active Brownian disks
Levis, Demian; Liebchen, Benno
JOURNAL OF PHYSICS-CONDENSED MATTER
30
084001
(2018)
Chiral active particles (or self-propelled circle swimmers) feature a rich collective behavior, comprising rotating macro-clusters and micro-flock patterns which consist of phase-synchronized rotating clusters with a characteristic self-limited size. These patterns emerge from the competition of alignment interactions and rotations suggesting that they might occur generically in many chiral active matter systems. However, although excluded volume interactions occur naturally among typical circle swimmers, it is not yet clear if macro-clusters and micro-flock patterns survive their presence. The present work shows that both types of pattern do survive but feature strongly enhance fluctuations regarding the size and shape of the individual clusters. Despite these fluctuations, we find that the average micro-flock size still follows the same characteristic scaling law as in the absence of excluded volume interactions, i.e. micro-flock sizes scale linearly with the single-swimmer radius.
Multiscale unfolding of real networks by geometric renormalization
G. García-Pérez, M. Boguñá i M. Á. Serrano
Nature Physics
(2018)
Symmetries in physical theories denote invariance under some transformation, such as self-similarity under a change of scale. The renormalization group provides a powerful framework to study these symmetries, leading to a better understanding of the universal properties of phase transitions. However, the small-world property of complex networks complicates application of the renormalization group by introducing correlations between coexisting scales. Here, we provide a framework for the investigation of complex networks at different resolutions. The approach is based on geometric representations, which have been shown to sustain network navigability and to reveal the mechanisms that govern network structure and evolution. We define a geometric renormalization group for networks by embedding them into an underlying hidden metric space. We find that real scale-free networks show geometric scaling under this renormalization group transformation. We unfold the networks in a self-similar multilayer shell that distinguishes the coexisting scales and their interactions. This in turn offers a basis for exploring critical phenomena and universality in complex networks. It also affords us immediate practical applications, including high-fidelity smaller-scale replicas of large networks and a multiscale navigation protocol in hyperbolic space, which betters those on single layers.
Quantitative account of social interactions in a mental health care ecosystem: cooperation, trust and collective action
Cigarini, Anna; Vicens, Julian; Duch, Jordi; Sanchez, Angel; Perello, Josep
SCIENTIFIC REPORTS
8
3794
(2018)
Mental disorders have an enormous impact in our society, both in personal terms and in the economic costs associated with their treatment. In order to scale up services and bring down costs, administrations are starting to promote social interactions as key to care provision. We analyze quantitatively the importance of communities for effective mental health care, considering all community members involved. By means of citizen science practices, we have designed a suite of games that allow to probe into different behavioral traits of the role groups of the ecosystem. The evidence reinforces the idea of community social capital, with caregivers and professionals playing a leading role. Yet, the cost of collective action is mainly supported by individuals with a mental condition - which unveils their vulnerability. The results are in general agreement with previous findings but, since we broaden the perspective of previous studies, we are also able to find marked differences in the social behavior of certain groups of mental disorders. We finally point to the conditions under which cooperation among members of the ecosystem is better sustained, suggesting how virtuous cycles of inclusion and participation can be promoted in a 'care in the community' framework.
Editorial for the topical issue on the Continuous Time Random Walk
Kutner, Ryszard; Masoliver, Jaume
EUROPEAN PHYSICAL JOURNAL B
91
40
(2018)
Contribution to the Topical Issue “Continuous Time Random Walk Still Trendy: Fifty-year History, Current State and Outlook”, edited by Ryszard Kutner and Jaume Masoliver.
Advances in colloidal manipulation and transport via hydrodynamic interactions
Martinez-Pedrero, F.; Tierno, P.
JOURNAL OF COLLOID AND INTERFACE SCIENCE
519
296 311
(2018)
In this review article, we highlight many recent advances in the field of micromanipulation of colloidal particles using hydrodynamic interactions (HIs), namely solvent mediated long-range interactions. At the micrsocale, the hydrodynamic laws are time reversible and the flow becomes laminar, features that allow precise manipulation and control of colloidal matter. We focus on different strategies where externally operated microstructures generate local flow fields that induce the advection and motion of the surrounding components. In addition, we review cases where the induced flow gives rise to hydrodynamic bound states that may synchronize during the process, a phenomenon essential in different systems such as those that exhibit self-assembly and swarming. (C) 2018 Elsevier Inc. All rights reserved.
Cuantificar las economías antiguas. Problemas y métodos.
Pérez González, J., Morvan M., Prignano, L., Morer, I., Díaz-Guilera, A., Bermúdez Lorenzo, J.M., Remesal Rodríguez, J.
INSTRUMENTA 60
251 280
(2018)
Reconstruir lo roto. Un método para vincular entre sí las inscripciones del Testaccio
Synchronization invariance under network structural transformations
Arola-Fernandez, L ; Diaz-Guilera, A; Arenas, A
PHYSICAL REVIEW E
97
060301
(2018)
Synchronization processes are ubiquitous despite the many connectivity patterns that complex systems can show. Usually, the emergence of synchrony is a macroscopic observable; however, the microscopic details of the system, as, e.g., the underlying network of interactions, is many times partially or totally unknown. We already know that different interaction structures can give rise to a common functionality, understood as a common macroscopic observable. Building upon this fact, here we propose network transformations that keep the collective behavior of a large system of Kuramoto oscillators invariant. We derive a method based on information theory principles, that allows us to adjust the weights of the structural interactions to map random homogeneous in-degree networks into random heterogeneous networks and vice versa, keeping synchronization values invariant. The results of the proposed transformations reveal an interesting principle; heterogeneous networks can be mapped to homogeneous ones with local information, but the reverse process needs to exploit higher-order information. The formalism provides analytical insight to tackle real complex scenarios when dealing with uncertainty in the measurements of the underlying connectivity structure.
Longitudinal Estimation of the Clinically Significant Change in the Treatment of Major Depression Disorder
Canete-Masse, C ; Pero-Cebollero, M ; Gudayol-Ferre, E; Guardia-Olmos, J
FRONTIERS IN PSYCHOLOGY
9
1406
(2018)
Background: Although major depressive disorder is usually treated with antidepressants, only 50-70% of the patients respond to this treatment. This study applied Jacobson and Truax's (1991) methodology (reliable change index, RCI) to a sample of depressive patients being treated with one of two antidepressants to evaluate their functioning and the effect of certain variables such as severity and age.
Method: Seventy-three depressive patients medicated with Escitalopram (n = 37) or Duloxetine (n = 36) were assessed using the Hamilton depression rating scale over a 24-week period.
Results: They indicate that the RCI stabilizes in an absolute way starting in week 16, and it is not until week 24 that all of the patients become part of the functional population. We found limited statistical significance with respect to the RCI and the external variables.
Conclusion: Our study suggests the need to accompany the traditional statistical methodology with some other clinical estimation systems capable of going beyond a simple subtraction between pre and posttreatment values. Hence, it is concluded that RCI estimations could be stronger and more stable than the classical statistical techniques.
A Complex Network Framework to Model Cognition: Unveiling Correlation Structures from Connectivity
Rosell-Tarrago, G ; Cozzo, E ; Diaz-Guilera, A
COMPLEXITY
1918753
(2018)
Several approaches to cognition and intelligence research rely on statistics-based model testing, namely, factor analysis. In the present work, we exploit the emerging dynamical system perspective putting the focus on the role of the network topology underlying the relationships between cognitive processes. We go through a couple of models of distinct cognitive phenomena and yet find the conditions for them to be mathematically equivalent. We find a nontrivial attractor of the system that corresponds to the exact definition of a well-known network centrality and hence stresses the interplay between the dynamics and the underlying network connectivity, showing that both of the two are relevant. Correlation matrices evince there must be a meaningful structure underlying real data. Nevertheless, the true architecture regarding the connectivity between cognitive processes is still a burning issue of research. Regardless of the network considered, it is always possible to recover a positive manifold of correlations. Furthermore, we show that different network topologies lead to different plausible statistical models concerning the correlation structure, ranging from one to multiple factor models and richer correlation structures.
Magnetically tunable bidirectional locomotion of a self-assembled nanorod-sphere propeller
Garcia-Torres, J ; Calero, C ; Sagues, F; Pagonabarraga, I ; Tierno, P
NATURE COMMUNICATIONS
9
1663
(2018)
Field-driven direct assembly of nanoscale matter has impact in disparate fields of science. In microscale systems, such concept has been recently exploited to optimize propulsion in viscous fluids. Despite the great potential offered by miniaturization, using self-assembly to achieve transport at the nanoscale remains an elusive task. Here we show that a hybrid propeller, composed by a ferromagnetic nanorod and a paramagnetic microsphere, can be steered in a fluid in a variety of modes, from pusher to puller, when the pair is dynamically actuated by a simple oscillating magnetic field. We exploit this unique design to build more complex structures capable of carrying several colloidal cargos as microscopic trains that quickly disassemble at will under magnetic command. In addition, our prototype can be extended to smaller nanorods below the diffraction limit, but still dynamically reconfigurable by the applied magnetic field.
Laning, thinning and thickening of sheared colloids in a two-dimensional Taylor-Couette geometry
Ortiz-Ambriz, A ; Gerloff, S ; Klapp, SHL ; Ortin, J; Tierno, P
SOFT MATTER
14
24
(2018)
We investigate the dynamics and rheological properties of a circular colloidal cluster that is continuously sheared by magnetic and optical torques in a two-dimensional (2D) Taylor-Couette geometry. By varying the two driving fields, we obtain the system flow diagram and report the velocity profiles along the colloidal structure. We then use the inner magnetic trimer as a microrheometer, and observe continuous thinning of all particle layers followed by thickening of the third one above a threshold field. Experimental data are supported by Brownian dynamics simulations. Our approach gives a unique microscopic view on how the structure of strongly confined colloidal matter weakens or strengthens upon shear, envisioning the engineering of rheological devices at the microscales.
A SYSTEMATIC REVIEW OF SIMULATION PROCEDURES FOR fMRI CONNECTIVITY STUDIES
Pero-Cebollero, M; Guardia-Olmos, J ; Mancho-Fora, N
ADVANCES AND APPLICATIONS IN STATISTICS
53
1
(2018)
The strategies used to study functional or effective connectivity in fMRI data are mainly based on the application of correlation studies, structural equation models (SEM), dynamic causal modelling (DCM), or the Granger causality model (GCM), while some contributions focus their attention on simulation studies. Although the tradition is scarce, this increase of the latter studies has become steeper in the last five years. In this work, we present a systematic study and analysis of simulation studies with fMRI data for the analysis of brain connectivity. We conducted a search on the Web of Science (WoS) and PubMed and eventually we reviewed a total of 134 studies. The most remarkable finding is a lack of information on the simulation procedure. For example, 17 works did not specify the model used to generate the signal, 36 did not indicate the model's white noise addition in the signal generated, and 52 did not detail the design under which the data had been generated. Under these circumstances, it is difficult to compare the different contributions in order to identify the best strategies to simulate data for the study of brain connectivity in fMRI works. However, it is important to note the emergence of the so-called third-generation simulation models, which consider the brain as a complex, dynamical system model. This kind of model to simulate brain activity will change the state of the art in this matter, and it might be a good tool to assess the different analytical procedures to study effective connectivity.
Assembly and transport of nematic colloidal swarms above photo-patterned defects and surfaces
Straube, AV; Pages, JM ; Ortiz-Ambriz, A; Tierno, P ; Ignes-Mullol, J ; Sagues, F
NEW JOURNAL OF PHYSICS
20
075006
(2018)
We investigate the dynamic assembly and swarm translocation of anisometric colloidal particles dispersed in a nematic liquid crystal and driven above a photosensitive surface. We use liquid crystal-enabled electrophoresis to propel these particles via an alternating electric field perpendicular to the sample cell. By manipulating the anchoring conditions on one surface of the experimental cell, we obtain a spatially extended spiral pattern of the liquid crystal orientation that induces the dynamic assembly of a rotating colloidal mill. This structure can be transported by translocating the topological defect above the photosensitive surface. We complement our findings with a theoretical model that captures the basic physics of the process, by formulating an analytic equation for the director field above the surface. Our reconfigurable nematic assemblies may be used as a test bed for complex swarming behaviour in biological and artificial microscale systems.
A dynamic vaccination strategy to suppress the recurrent epidemic outbreaks
Chen, DD ; Zheng, MH; Zhao, M ; Zhang, Y
CHAOS SOLITONS & FRACTALS
113
108 114
(2018)
Efficient vaccination strategy is crucial for controlling recurrent epidemic spreading on networks. In this paper, based on the analysis of real epidemic data and simulations, it's found that the risk indicator of recurrent epidemic outbreaks could be determined by the ratio of the epidemic infection rate of the year to the average infected density of the former year. According to the risk indicator, the dynamic vaccination probability of each year can be designed to suppress the epidemic outbreaks. Our simulation results show that the dynamic vaccination strategy could effectively decrease the maximal and average infected density, and meanwhile increase the time intervals of epidemic outbreaks and individuals attacked by epidemic. In addition, our results indicate that to depress the influenza outbreaks, it is not necessary to keep the vaccination probability high every year; and adjusting the vaccination probability at right time could decrease the outbreak risks with lower costs. Our findings may present a theoretical guidance for the government and the public to control the recurrent epidemic outbreaks. (C) 2018 Elsevier Ltd. All rights reserved.
Metabolic plasticity in synthetic lethal mutants: Viability at higher cost
Massucci, FA ; Sagues, F ; Serrano, MA
PLOS COMPUTATIONAL BIOLOGY
14
e1005949
(2018)
The most frequent form of pairwise synthetic lethality (SL) in metabolic networks is known as plasticity synthetic lethality. It occurs when the simultaneous inhibition of paired functional and silent metabolic reactions or genes is lethal, while the default of the functional partner is backed up by the activation of the silent one. Using computational techniques on bacterial genome-scale metabolic reconstructions, we found that the failure of the functional partner triggers a critical reorganization of fluxes to ensure viability in the mutant which not only affects the SL pair but a significant fraction of other interconnected reactions, forming what we call a SL cluster. Interestingly, SL clusters show a strong entanglement both in terms of reactions and genes. This strong overlap mitigates the acquired vulnerabilities and increased structural and functional costs that pay for the robustness provided by essential plasticity. Finally, the participation of coessential reactions and genes in different SL clusters is very heterogeneous and those at the intersection of many SL clusters could serve as supertargets for more efficient drug action in the treatment of complex diseases and to elucidate improved strategies directed to reduce undesired resistance to chemicals in pathogens.
Epidemic spreading in modular time-varying networks
Nadini, M ; Sun, KY ; Ubaldi, E ; Starnini, M ; Rizzo, A ; Perra, N
SCIENTIFIC REPORTS
8
2352
(2018)
We investigate the effects of modular and temporal connectivity patterns on epidemic spreading. To this end, we introduce and analytically characterise a model of time-varying networks with tunable modularity. Within this framework, we study the epidemic size of Susceptible-Infected-Recovered, SIR, models and the epidemic threshold of Susceptible-Infected-Susceptible, SIS, models. Interestingly, we find that while the presence of tightly connected clusters inhibits SIR processes, it speeds up SIS phenomena. In this case, we observe that modular structures induce a reduction of the threshold with respect to time-varying networks without communities. We confirm the theoretical results by means of extensive numerical simulations both on synthetic graphs as well as on a real modular and temporal network.
Collective motion of active Brownian particles with polar alignment
Martin-Gomez, A ; Levis, D ; Diaz-Guilera, A ; Pagonabarraga, I
SOFT MATTER
14
14
(2018)
We present a comprehensive computational study of the collective behavior emerging from the competition between self-propulsion, excluded volume interactions and velocity-alignment in a two-dimensional model of active particles. We consider an extension of the active brownian particles model where the self-propulsion direction of the particles aligns with the one of their neighbors. We analyze the onset of collective motion (flocking) in a low-density regime (10% surface area) and show that it is mainly controlled by the strength of velocity-alignment interactions: the competition between self-propulsion and crowding effects plays a minor role in the emergence of flocking. However, above the flocking threshold, the system presents a richer pattern formation scenario than analogous models without alignment interactions (active brownian particles) or excluded volume effects (Vicsek-like models). Depending on the parameter regime, the structure of the system is characterized by either a broad distribution of finite-sized polar clusters or the presence of an amorphous, highly fluctuating, large-scale traveling structure which can take a lane-like or band-like form (and usually a hybrid structure which is halfway in between both). We establish a phase diagram that summarizes collective behavior of polar active brownian particles and propose a generic mechanism to describe the complexity of the large-scale structures observed in systems of repulsive self-propelled particles.
Rheological behavior of colloidal suspension with long-range interactions
Arietaleaniz, S ; Malgaretti, P ; Pagonabarraga, I ; Hidalgo, RC
PHYSICAL REVIEW E
98
042603
(2018)
In this work, we study the constitutive behavior of interacting colloidal suspensions for moderate and high concentrations. Specifically, using a lattice Boltzmann solver, we numerically examine suspensions flowing through narrow channels, and explore the significance of the interaction potential strength on the system's macroscopic response. When only a short-range interaction potential is considered, a Newtonian behavior is always recovered and the system's effective viscosity mostly depends on the suspension concentration. However, when using a Lennard-Jones potential we identify two rheological responses depending on the interaction strength, the volume fraction, and the pressure drop. Exploiting a model proposed in the literature we rationalize the simulation data and propose scaling relations to identify the relevant energy scales involved in these transport processes. Moreover, we find that the spatial distribution of colloids in layers parallel to the flow direction does not correlate with changes in the system macroscopic response; but, interestingly, the rheology changes do correlate with the spatial distribution of colloids within individual layers. Namely, suspensions characterized by a Newtonian response display a cubiclike structure of the colloids within individual layers, whereas for suspensions with non-Newtonian response colloids organize in a hexagonal structure.
Unraveling the Operational Mechanisms of Chemically Propelled Motors with Micropumps
Esplandiu, MJ ; Zhang, K ; Fraxedas, J ; Sepulveda, B ; Reguera, D
ACCOUNTS OF CHEMICAL RESEARCH
51
1921 1930
(2018)
CONSPECTUS: The development of effective autonomous micro- and nanomotors relies on controlling fluid motion at interfaces. One of the main challenges in the engineering of such artificial machines is the quest for efficient mechanisms to power them without using external driving forces. In the past decade, there has been an important increase of man-made micro- and nanomotors fueled by self-generated physicochemical gradients. Impressive proofs of concept of multitasking machines have been reported demonstrating their capabilities for a plethora of applications. While the progress toward applications is promising, there are still open questions on fundamental physicochemical aspects behind the mechanical actuation, which require more experimental and theoretical efforts. These efforts are not merely academic but will open the door for an efficient and practical implementation of such promising devices.
In this Account, we focus on chemically driven motors whose motion is the result of a complex interplay of chemical reactions and (electro)hydrodynamic phenomena. A reliable study of these processes is rather difficult with mobile objects like swimming motors. However, pumps, which are the immobilized motor counterparts, emerge as simple manufacturing and well-defined platforms for a better experimental probing of the mechanisms and key parameters controlling the actuation.
Here we review some recent studies using a new methodology that has turned out to be very helpful to characterize micropump chemomechanics. The aim was to identify the redox role of the motor components, to map the chemical reaction, and to quantify the relevant electrokinetic parameters (e.g., electric field and fluid flow). This was achieved by monitoring the velocity of differently charged tracers and by fluorescence imaging of the chemical species involved in the chemical reaction, for example, proton gradients. We applied these techniques to different systems of interest. First, we probed bimetallic pumps as counterparts of the pioneering bimetallic swimmers. We corroborated that fluid motion was due to a self-generated electro-osmotic mechanism driven by the redox decomposition of H2O2. In addition, we analyzed by simulations the key parameters that yield an optimized operation. Moreover, we accomplished a better assessment of the importance of surface chemistry on the metal electrochemical response, highlighting its relevance in controlling the redox role of the metals and motion direction.
Second, we focused on metallic and semiconductor micropumps to analyze light-controlled motion mechanisms through photoelectrochemical decomposition of fuels. These pumps were driven by visible light and could operate using just water as fuel. In these systems, we found a very interesting competition between two different mechanisms for fluid propulsion, namely, light-activated electro-osmosis and light-insensitive diffusio-osmosis, stemming from different chemical pathways in the fuel decomposition. In this case, surface roughness becomes a pivotal parameter to enhance or depress one mechanism over the other.
These examples demonstrate that pumps are practical platforms to explore operating mechanisms and to quantify their performance. Additionally, they are suitable systems to test novel fuels or motor materials. This knowledge is extensible to swimmers providing not only fundamental understanding of their locomotion mechanisms but also useful clues for their design and optimization.
Statistical Physics Approach to Thermophoresis of Colloids
Sancho, JM
JOURNAL OF STATISTICAL PHYSICS
172
6
(2018)
Brownian colloidal particles dispersed in a fluid with a temperature gradient experience a driving force which produces a large biased steady density profile. This phenomenon, named thermophoresis, is quantified by the Soret coefficient S-T. This problem is studied here within the Fokker-Planck formalism. Using the experimental observation of S-T it is possible to extract relevant analytical information about a temperature dependent nonequilibrium effective potential which can produce the thermophoresis force. It will be presented here a statistical physics derivation of this effective potential in terms of some physical parameters. Experimental data are analyzed within this theoretical scenario.
Full Phase Diagram of Active Brownian Disks: From Melting to Motility-Induced Phase Separation
Digregorio, P ; Levis, D ; Suma, A ; Cugliandolo, LF ; Gonnella, G ; Pagonabarraga, I
PHYSICAL REVIEW LETTERS
121
098003
(2018)
We establish the complete phase diagram of self-propelled hard disks in two spatial dimensions from the analysis of the equation of state and the statistics of local order parameters. The equilibrium melting scenario is maintained at small activities, with coexistence between active liquid and hexatic order, followed by a proper hexatic phase, and a further transition to an active solid. As activity increases, the emergence of hexatic and solid order is shifted towards higher densities. Above a critical activity and for a certain range of packing fractions, the system undergoes motility-induced phase separation and demixes into low and high density phases; the latter can be either disordered (liquid) or ordered (hexatic or solid) depending on the activity.
Double transition of information spreading in a two-layered network
Wu, J; Zheng, MH ; Wang, W ; Yang, HJ ; Gu, CG
CHAOS
28
083117
(2018)
A great deal of significant progress has been seen in the study of information spreading on populations of networked individuals. A common point in many of the past studies is that there is only one transition in the phase diagram of the final accepted size versus the transmission probability. However, whether other factors alter this phenomenology is still under debate, especially for the case of information spreading through many channels and platforms. In the present study, we adopt a two-layered network to represent the interactions of multiple channels and propose a Susceptible-Accepted-Recovered information spreading model. Interestingly, our model shows a novel double transition including a continuous transition and a following discontinuous transition in the phase diagram, which originates from two outbreaks between the two layers of the network. Furthermore, we reveal that the key factors are a weak coupling condition between the two layers, a large adoption threshold, and the difference of the degree distributions between the two layers. Moreover, we also test the model in the coupled empirical social networks and find similar results as in the synthetic networks. Then, an edge-based compartmental theory is developed which fully explains all numerical results. Our findings may be of significance for understanding the secondary outbreaks of information in real life. Published by AIP Publishing.
Impact of modular organization on dynamical richness in cortical networks
Yamamoto, Hideaki; Moriya, Satoshi; Ide, Katsuya; Hayakawa, Takeshi; Akima, Hisanao; Sato, Shigeo; Kubota, Shigeru; Tanii, Takashi; Niwano, Michio; Teller, Sara; Soriano, Jordi; Hirano-Iwata, Ayumi
Science Advances
4
11
(2018)
As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.
Resource heterogeneity leads to unjust effort distribution in climate change mitigation
Vicens, J ; Bueno-Guerra, N ; Gutierrez-Roig, M ; Gracia-Lazaro, C ; Gomez-Gardenes, J ; Perello, J ; Sanchez, A ; Moreno, Y ; Duch, J
PLOS ONE
13
10
(2018)
Climate change mitigation is a shared global challenge that involves collective action of a set of individuals with different tendencies to cooperation. However, we lack an understanding of the effect of resource inequality when diverse actors interact together towards a common goal. Here, we report the results of a collective-risk dilemma experiment in which groups of individuals were initially given either equal or unequal endowments. We found that the effort distribution was highly inequitable, with participants with fewer resources contributing significantly more to the public goods than the richer -sometimes twice as much. An unsupervised learning algorithm classified the subjects according to their individual behavior, finding the poorest participants within two "generous clusters" and the richest into a "greedy cluster". Our results suggest that policies would benefit from educating about fairness and reinforcing climate justice actions addressed to vulnerable people instead of focusing on understanding generic or global climate consequences.